| Title | Combined Neural Network Based on Deep Learning for AMR |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Li, Pei, Wang, Longlong |
| Conference Name | 2021 7th International Conference on Computer and Communications (ICCC) |
| Keywords | Automatic modulation recognition (AMR), combined neural network, convolutional neural networks, Deep Learning, Digital modulation, Learning systems, Network reconnaissance, Neural networks, pubcrawl, Reconnaissance, resilience, Resiliency, Scalability, Software, USRP |
| Abstract | Automatic modulation recognition (AMR) plays an important role in cognitive radio and electronic reconnaissance applications. In order to solve the problem that the lack of modulation signal data sets, the labeled data sets are generated by the software radio equipment NI-USRP 2920 and LabVIEW software development tool. In this paper, a combined network based on deep learning is proposed to identify ten types of digital modulation signals. Convolutional neural network (CNN) and Inception network are trained on different data sets, respectively. We combine CNN with Inception network to distinguish different modulation signals well. Experimental results show that our proposed method can recognize ten types of digital modulation signals with high identification accuracy, even in scenarios with a low signal-to-noise ratio (SNR). |
| DOI | 10.1109/ICCC54389.2021.9674421 |
| Citation Key | li_combined_2021 |
Combined Neural Network Based on Deep Learning for AMR